Exploratory Study of the Download Speed of Leading University Hospitality and Tourism Department Websites Worldwide

Increased broadband penetration (BP) rates around the world have encouraged web designers to include more web content and additional functions on their web sites, thereby enhancing the richness and playfulness of the information. However, it is often very difficult for web surfers who are still using narrowband connections to access such web sites. Many university web sites target international audiences; therefore their download performance should be considered, as it may directly influence the user experience. This exploratory study examined 331 university hospitality and tourism department web sites in 37 countries. The empirical results showed that entry web pages of universities in Asia, with a medium BP rate (mid-BP), have the slowest download speeds, and those in Australia and New Zealand perform the best. The adoption rate of the Cascade Style Sheet (CSS) in Asia is relatively lower than that of other regions

A taxonomy of web prediction algorithms

Web prefetching techniques are an attractive solution to reduce the user-perceived latency. These techniques are driven by a prediction engine or algorithm that guesses following actions of web users. A large amount of prediction algorithms has been proposed since the first prefetching approach was published, although it is only over the last two or three years when they have begun to be successfully implemented in commercial products. These algorithms can be implemented in any element of the web architecture and can use a wide variety of information as input. This affects their structure, data system, computational resources and accuracy. The knowledge of the input information and the understanding of how it can be handled to make predictions can help to improve the design of current prediction engines, and consequently prefetching techniques. This paper analyzes fifty of the most relevant algorithms proposed along 15 years of prefetching research and proposes a taxonomy where the algorithms are classified according to the input data they use. For each group, the main advantages and shortcomings are highlighted. © 2012 Elsevier Ltd. All rights reserved.This work has been partially supported by Spanish Ministry of Science and Innovation under Grant TIN2009-08201, Generalitat Valenciana under Grant GV/2011/002 and Universitat Politecnica de Valencia under Grant PAID-06-10/2424.Domenech, J.; De La Ossa Perez, BA.; Sahuquillo Borrás, J.; Gil Salinas, JA.; Pont Sanjuan, A. (2012). A taxonomy of web prediction algorithms. Expert Systems with Applications. 39(9):8496-8502. https://doi.org/10.1016/j.eswa.2012.01.140S8496850239